Abstract: A set of wavelet frames generated by Gaussian radial basis functions are presented. It is constructively proved that a radial basis function network with Gaussian activation functions can approximate any function in L2 (Rd) with desired accuracy. Furthermore, an adaptive learning algorithm for constructing and training networks is proposed based on time-frequency localization properties of Gaussian radial basis functions and the adaptive projection algorithm. Applications to signal reconstruction and noise elimination are given.